Trajectory Object Detection using Deep Learning Algorithms
S Anitha Elavarasi1, J Jayanthi2, N Basker3
1S Anitha Elavarasi, Department of Computer Science & Engineering, Sona College of Technology, Salem, India.
2J Jayanthi, Department of Computer Science & Engineering, Sona College of Technology, Salem, India.
3N Basker, Department of Computer Science & Engineering, Sona College of Technology, Salem, India.
Manuscript received on 06 August 2019. | Revised Manuscript received on 12 August 2019. | Manuscript published on 30 September 2019. | PP: 7895-7898 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6564098319/2019©BEIESP | DOI: 10.35940/ijrte.C6564.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Video surveillance data in smart cities needs to analyze a large amount of video footage in order to locate the people who are violating the traffic rules. The fact is that it is very easy for the human being to recognize different objects in images and videos. For a computer program this is quite a difficult task. Hence there is a need for visual big data analytics which involves processing and analyzing large scale visual data such as images or videos. One major application of trajectory object detection is the Intelligent Transport Systems (ITS). Vehicle type detection, tracking and classification play an important role in ITS. In order to analyze huge amount of video footage deep learning algorithms have been deployed. The main phase of vehicle type detection includes annotating the data, training the model and validating the model. The problems and challenges in identifying or detecting type of vehicle are due to weather, shadows, blurring effect, light condition and quality of the data. In this paper deep learning algorithms such as Faster R CNN and Mask R CNN and Frameworks like YOLO were used for the object detection. Dataset (different types of vehicle pictures in video format) were collected both from in-house premises as well as from the Internet to detect and recognize the type of vehicles which are common in traffic systems. The experimental results show that among the three approaches used the Mask R CNN algorithm is found to be more efficient and accurate in vehicle type detection.
Keywords: Deep Learning, Intelligent Transport Systems, Mask RCNN, YOLO
Scope of the Article: Deep Learning